Modern global cellular and non-cellular positioning technologies are based on generating large global databases containing information on cellular and non-cellular signals. The information may originate entirely or partially from users of these positioning technologies. This approach is also referred to as “crowd-sourcing”.
The positioning is based on the fact, that different locations have different measurement statistics or “fingerprints”. In a first phase or training phase, the users with their mobile terminals may provide information in the form of such fingerprints. The fingerprints may contain on the one hand location information that is estimated based on, e.g., received satellite signals of a global navigation satellite system (GNSS) and on the other hand measurement data taken from one or more transmitters, such as radio interfaces for signals of a cellular and/or non-cellular terrestrial system. Such measurement data may contain the received signal strength (RSS), for instance.
However, even with the help of crowd-sourcing, the collection of measurement data from transmitters requires extensive measurement campaigns and continuous updating of the acquired data base. As a result, the data collection might be limited to some specific areas, which leaves coverage gaps in the measurement data. In addition, some of the areas to be included in the data base might have restricted access preventing the possibility to collect measurement data.
Therefore, this data may then be transferred to a server or cloud, where the data may be collected and where models may be generated based on the collected data for positioning purposes. This may be a continuous background process, in which the mobile terminals continuously report measurement data to a server or they may learn the radio environment internally offline. Such models can be coverage area estimates, transmitter positions and/or radio channel models, with base stations of cellular communication system and access points of WLANs being exemplary transmitters. Thus, even areas, where measurement data is not available, can be estimated by appropriate interpolation and extrapolation methods. Here, the interpolation refers to the estimation of data values between known data values, and extrapolation refers to the estimation of data values outside the known values.
In a second phase or positioning phase, these models may be used for estimating the position of mobile terminals, for example. For instance, the position of a network user can be estimated by comparing its own measurement data with the prediction of a generated model, which was derived from the fingerprints of the first phase or training phase. The interpolation and extrapolation process enables user positioning outside the coverage area of the available measurement data, which reduces the requirements of the data collection.
However, such fingerprints do not necessarily have to comprise a GNSS based location information. They could additionally or alternatively include location information based on cellular and/or WLAN only. In this case the fingerprint could be assigned a position for example based on a WLAN based positioning in a server. Such self-positioned fingerprints can be used to learn cellular system information, in case there are cellular measurements in the fingerprint. Moreover, in a set of WLAN measurements in a fingerprint there may be, in addition to measurements for known WLAN access points, also measurements for unknown access points, and the position of the unknown access points can be learned through these self-positioned fingerprints. Finally, more data can be taken for previously known access points based on self-positioned fingerprints.
It may be noted that even when using a mobile terminal having GNSS-capabilities, a user may benefit from using cellular/non-cellular positioning technologies in terms of time-to-first-fix and power consumption. Also, not all applications require a GNSS-based position. Furthermore, cellular/non-cellular positioning technologies work indoors as well, which is generally a challenging environment for GNSS-based technologies.
To this regard, one considerable difference in indoor and outdoor positioning is the importance of the vertical direction. In outdoor positioning it is often enough to achieve horizontal position estimates using two dimensional models. However, indoors, especially in tall buildings, it is essential to have capabilities to estimate also the floor the user is located on. This leads to three dimensional data processing both in the first phase or training phase and in the second phase or positioning phase.